'''simple docstring'''




import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


__UpperCAmelCase					=get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__UpperCAmelCase					=get_tests_dir("fixtures/vocab.json")
__UpperCAmelCase					=get_tests_dir("fixtures")




class        a__ (  unittest.TestCase					):
     lowerCamelCase    :	Dict				=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    0






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''       )
         self.assertIsInstance(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCamelCase            =    WavaVecaConfig()
             __lowerCamelCase            =    AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''       )

             # save in new folder
             model_config.save_pretrained(a       )
             processor.save_pretrained(a       )

             __lowerCamelCase            =    AutoProcessor.from_pretrained(a       )

         self.assertIsInstance(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         with tempfile.TemporaryDirectory() as tmpdirname:
             # copy relevant files
             copyfile(a    ,							os.path.join(a    ,							a       )       )
             copyfile(a    ,							os.path.join(a    ,							'''vocab.json'''       )       )

             __lowerCamelCase            =    AutoProcessor.from_pretrained(a       )

         self.assertIsInstance(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCamelCase            =    WavaVecaFeatureExtractor()
             __lowerCamelCase            =    AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''       )

             __lowerCamelCase            =    WavaVecaProcessor(a    ,							a       )

             # save in new folder
             processor.save_pretrained(a       )

             # drop `processor_class` in tokenizer
             with open(os.path.join(a    ,							a       )    ,							'''r'''       ) as f:
                 __lowerCamelCase            =    json.load(a       )
                 config_dict.pop('''processor_class'''       )

             with open(os.path.join(a    ,							a       )    ,							'''w'''       ) as f:
                 f.write(json.dumps(a       )       )

             __lowerCamelCase            =    AutoProcessor.from_pretrained(a       )

         self.assertIsInstance(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCamelCase            =    WavaVecaFeatureExtractor()
             __lowerCamelCase            =    AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''       )

             __lowerCamelCase            =    WavaVecaProcessor(a    ,							a       )

             # save in new folder
             processor.save_pretrained(a       )

             # drop `processor_class` in feature extractor
             with open(os.path.join(a    ,							a       )    ,							'''r'''       ) as f:
                 __lowerCamelCase            =    json.load(a       )
                 config_dict.pop('''processor_class'''       )

             with open(os.path.join(a    ,							a       )    ,							'''w'''       ) as f:
                 f.write(json.dumps(a       )       )

             __lowerCamelCase            =    AutoProcessor.from_pretrained(a       )

         self.assertIsInstance(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCamelCase            =    WavaVecaConfig(processor_class='''Wav2Vec2Processor'''       )
             model_config.save_pretrained(a       )
             # copy relevant files
             copyfile(a    ,							os.path.join(a    ,							'''vocab.json'''       )       )
             # create emtpy sample processor
             with open(os.path.join(a    ,							a       )    ,							'''w'''       ) as f:
                 f.write('''{}'''       )

             __lowerCamelCase            =    AutoProcessor.from_pretrained(a       )

         self.assertIsInstance(a    ,							a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         with self.assertRaises(a       ):
             __lowerCamelCase            =    AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''       )
         # If remote code is disabled, we can't load this config.
         with self.assertRaises(a       ):
             __lowerCamelCase            =    AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''    ,							trust_remote_code=a       )

         __lowerCamelCase            =    AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''    ,							trust_remote_code=a       )
         self.assertTrue(processor.special_attribute_present       )
         self.assertEqual(processor.__class__.__name__    ,							'''NewProcessor'''       )

         __lowerCamelCase            =    processor.feature_extractor
         self.assertTrue(feature_extractor.special_attribute_present       )
         self.assertEqual(feature_extractor.__class__.__name__    ,							'''NewFeatureExtractor'''       )

         __lowerCamelCase            =    processor.tokenizer
         self.assertTrue(tokenizer.special_attribute_present       )
         if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__    ,							'''NewTokenizerFast'''       )

             # Test we can also load the slow version
             __lowerCamelCase            =    AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''    ,							trust_remote_code=a    ,							use_fast=a       )
             __lowerCamelCase            =    new_processor.tokenizer
             self.assertTrue(new_tokenizer.special_attribute_present       )
             self.assertEqual(new_tokenizer.__class__.__name__    ,							'''NewTokenizer'''       )
         else:
             self.assertEqual(tokenizer.__class__.__name__    ,							'''NewTokenizer'''       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         try:
             AutoConfig.register('''custom'''    ,							a       )
             AutoFeatureExtractor.register(a    ,							a       )
             AutoTokenizer.register(a    ,							slow_tokenizer_class=a       )
             AutoProcessor.register(a    ,							a       )
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(a       ):
                 AutoProcessor.register(a    ,							a       )

             # Now that the config is registered, it can be used as any other config with the auto-API
             __lowerCamelCase            =    CustomFeatureExtractor.from_pretrained(a       )

             with tempfile.TemporaryDirectory() as tmp_dir:
                 __lowerCamelCase            =    os.path.join(a    ,							'''vocab.txt'''       )
                 with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as vocab_writer:
                     vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]       )       )
                 __lowerCamelCase            =    CustomTokenizer(a       )

             __lowerCamelCase            =    CustomProcessor(a    ,							a       )

             with tempfile.TemporaryDirectory() as tmp_dir:
                 processor.save_pretrained(a       )
                 __lowerCamelCase            =    AutoProcessor.from_pretrained(a       )
                 self.assertIsInstance(a    ,							a       )

         finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                 del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                 del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
             if CustomConfig in PROCESSOR_MAPPING._extra_content:
                 del PROCESSOR_MAPPING._extra_content[CustomConfig]






     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         class        a__ (  UpperCAmelCase__					):
              lowerCamelCase    :	Union[str, Any]				=False




         class        a__ (  UpperCAmelCase__					):
              lowerCamelCase    :	Dict				=False




         class        a__ (  UpperCAmelCase__					):
              lowerCamelCase    :	Any				="AutoFeatureExtractor"
              lowerCamelCase    :	int				="AutoTokenizer"
              lowerCamelCase    :	List[Any]				=False

         try:
             AutoConfig.register('''custom'''    ,							a       )
             AutoFeatureExtractor.register(a    ,							a       )
             AutoTokenizer.register(a    ,							slow_tokenizer_class=a       )
             AutoProcessor.register(a    ,							a       )
             # If remote code is not set, the default is to use local classes.
             __lowerCamelCase            =    AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''       )
             self.assertEqual(processor.__class__.__name__    ,							'''NewProcessor'''       )
             self.assertFalse(processor.special_attribute_present       )
             self.assertFalse(processor.feature_extractor.special_attribute_present       )
             self.assertFalse(processor.tokenizer.special_attribute_present       )

             # If remote code is disabled, we load the local ones.
             __lowerCamelCase            =    AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''    ,							trust_remote_code=a       )
             self.assertEqual(processor.__class__.__name__    ,							'''NewProcessor'''       )
             self.assertFalse(processor.special_attribute_present       )
             self.assertFalse(processor.feature_extractor.special_attribute_present       )
             self.assertFalse(processor.tokenizer.special_attribute_present       )

             # If remote is enabled, we load from the Hub.
             __lowerCamelCase            =    AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''    ,							trust_remote_code=a       )
             self.assertEqual(processor.__class__.__name__    ,							'''NewProcessor'''       )
             self.assertTrue(processor.special_attribute_present       )
             self.assertTrue(processor.feature_extractor.special_attribute_present       )
             self.assertTrue(processor.tokenizer.special_attribute_present       )

         finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                 del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                 del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
             if CustomConfig in PROCESSOR_MAPPING._extra_content:
                 del PROCESSOR_MAPPING._extra_content[CustomConfig]






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''       )
         self.assertEqual(processor.__class__.__name__    ,							'''BertTokenizerFast'''       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''       )
         self.assertEqual(processor.__class__.__name__    ,							'''ConvNextImageProcessor'''       )




@is_staging_test
class        a__ (  unittest.TestCase					):
     lowerCamelCase    :	int				=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






     @classmethod
     def   SCREAMING_SNAKE_CASE__  (					cls						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    TOKEN
         HfFolder.save_token(a       )






     @classmethod
     def   SCREAMING_SNAKE_CASE__  (					cls						:						Dict       ):





         """simple docstring"""



         try:
             delete_repo(token=cls._token    ,							repo_id='''test-processor'''       )
         except HTTPError:
             pass

         try:
             delete_repo(token=cls._token    ,							repo_id='''valid_org/test-processor-org'''       )
         except HTTPError:
             pass

         try:
             delete_repo(token=cls._token    ,							repo_id='''test-dynamic-processor'''       )
         except HTTPError:
             pass






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    WavaVecaProcessor.from_pretrained(a       )
         with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(
                 os.path.join(a    ,							'''test-processor'''       )    ,							push_to_hub=a    ,							use_auth_token=self._token       )

             __lowerCamelCase            =    WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""       )
             for k, v in processor.feature_extractor.__dict__.items():
                 self.assertEqual(a    ,							getattr(new_processor.feature_extractor    ,							a       )       )
             self.assertDictEqual(new_processor.tokenizer.get_vocab()    ,							processor.tokenizer.get_vocab()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple       ):





         """simple docstring"""



         __lowerCamelCase            =    WavaVecaProcessor.from_pretrained(a       )

         with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(
                 os.path.join(a    ,							'''test-processor-org'''       )    ,							push_to_hub=a    ,							use_auth_token=self._token    ,							organization='''valid_org'''    ,							)

             __lowerCamelCase            =    WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''       )
             for k, v in processor.feature_extractor.__dict__.items():
                 self.assertEqual(a    ,							getattr(new_processor.feature_extractor    ,							a       )       )
             self.assertDictEqual(new_processor.tokenizer.get_vocab()    ,							processor.tokenizer.get_vocab()       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         CustomFeatureExtractor.register_for_auto_class()
         CustomTokenizer.register_for_auto_class()
         CustomProcessor.register_for_auto_class()

         __lowerCamelCase            =    CustomFeatureExtractor.from_pretrained(a       )

         with tempfile.TemporaryDirectory() as tmp_dir:
             __lowerCamelCase            =    os.path.join(a    ,							'''vocab.txt'''       )
             with open(a    ,							'''w'''    ,							encoding='''utf-8'''       ) as vocab_writer:
                 vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]       )       )
             __lowerCamelCase            =    CustomTokenizer(a       )

         __lowerCamelCase            =    CustomProcessor(a    ,							a       )

         with tempfile.TemporaryDirectory() as tmp_dir:
             create_repo(f"""{USER}/test-dynamic-processor"""    ,							token=self._token       )
             __lowerCamelCase            =    Repository(a    ,							clone_from=f"""{USER}/test-dynamic-processor"""    ,							token=self._token       )
             processor.save_pretrained(a       )

             # This has added the proper auto_map field to the feature extractor config
             self.assertDictEqual(
                 processor.feature_extractor.auto_map    ,							{
                     '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
                     '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                 }    ,							)

             # This has added the proper auto_map field to the tokenizer config
             with open(os.path.join(a    ,							'''tokenizer_config.json'''       )       ) as f:
                 __lowerCamelCase            =    json.load(a       )
             self.assertDictEqual(
                 tokenizer_config['''auto_map''']    ,							{
                     '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                     '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                 }    ,							)

             # The code has been copied from fixtures
             self.assertTrue(os.path.isfile(os.path.join(a    ,							'''custom_feature_extraction.py'''       )       )       )
             self.assertTrue(os.path.isfile(os.path.join(a    ,							'''custom_tokenization.py'''       )       )       )
             self.assertTrue(os.path.isfile(os.path.join(a    ,							'''custom_processing.py'''       )       )       )

             repo.push_to_hub()

         __lowerCamelCase            =    AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""    ,							trust_remote_code=a       )
         # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
         self.assertEqual(new_processor.__class__.__name__    ,							'''CustomProcessor'''       )

'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="levit"






     def __init__(					self						:						Optional[int]    ,							a						:						int=2_24    ,							a						:						Union[str, Any]=3    ,							a						:						List[Any]=3    ,							a						:						Dict=2    ,							a						:						Tuple=1    ,							a						:						Optional[int]=16    ,							a						:						Optional[Any]=[1_28, 2_56, 3_84]    ,							a						:						Dict=[4, 8, 12]    ,							a						:						Optional[Any]=[4, 4, 4]    ,							a						:						Optional[int]=[16, 16, 16]    ,							a						:						Any=0    ,							a						:						List[Any]=[2, 2, 2]    ,							a						:						Optional[Any]=[2, 2, 2]    ,							a						:						Union[str, Any]=0.02    ,							**a						:						Optional[int]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    kernel_size
         __lowerCamelCase            =    stride
         __lowerCamelCase            =    padding
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    num_attention_heads
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    key_dim
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    attention_ratio
         __lowerCamelCase            =    mlp_ratio
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    [
             ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Tuple				=version.parse("1.11"					)






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         return 1e-4
